Genotype by feeding system interaction in the genetic evaluation of Jersey cattle for milk yield

Abstract

Results of studies in dairy cattle about the magnitude of the genotype-environment interaction (GEI) are variable, depending on the definitions of genotype and environment. Therefore, the objective of this study was to determine the magnitude of the interaction of genotype and feeding system (confinement and grazing) in the Mexican genetic evaluation of Jersey cattle for milk yield. The number of lactations and animals in the pedigree used were 5122 and 18 432. An animal model and the MTDFREML program were used to estimate genetic parameters and predict genetic values of the animals. Bivariate analysis was carried out considering the performance of confined and grazing cows as two different traits. Three indicator variables were used to assess GEI: (i) magnitude of the genetic correlation coefficients, (ii) correlation between predicted breeding values and (iii) frequency of coincidence in the ranking of top sires. The magnitude of GEI depended on the choice of the indicator variable. The estimate of genetic correlation coefficient less than unity (0.76; P , 0.05) suggested the presence of biologically important GEI. The differences in phenotypic averages and variances between confinement and grazing systems seem to be the main causes for the genotype by environment interaction detected. However, the correlation coefficient between breeding values from confined and grazing animals (0.96) and the frequency of coincidence between breeding values of common sires within the top 100 in confinement and grazing (0.86) indicated low-to-moderate re-ranking of animals or top sires. In addition, the high correlations between predicted breeding values of Mexican genetic evaluation and the two environments (0.99 and 0.93 for confinement and grazing) indicated that for the two feeding systems, breeding values from national analyses could be safely used.

Parental Effects on Epigenetic Programming in Gametes and Embryos of Dairy Cows

“…However, emerging evidence using genetic correlation proved the existence of re-ranking effects of G × E interactions, especially between the traits expressed in environments with large differences as reviewed by Hammami et al (2009b) and Wakchaure et al (2016) . Genetic correlations < 0.80 were observed between milk yield across countries ( Ojango and Pollott, 2002 ; Cerón-Muñoz et al, 2004b ; Hammami et al, 2009a ), between age at first calving across countries ( Cerón-Muñoz et al, 2004a ), between two feeding systems within country ( Ramírez-Valverde et al, 2010 ), indicating the presence of genotype by environment interactions and at least some re-ranking of the animals. In this case, the breeders are required to optimize the breeding programs to accommodate the various environments.…”

Impacto de las evaluaciones genéticas en las tendencias genéticas de bovinos Jersey y Suizo Americano en México

“…a las de México puede reducir la respuesta esperada, especialmente para las poblaciones JE y SA mexicanas (Ramírez-Valverde et al, 2014b). Lo anterior, debido a la presencia significativa de interacción genotipo por ambiente reportada en poblaciones JE y SA, la cual se magnifica cuando las diferencias en los ambientes se incrementan (Ramírez-Valverde et al, 2010;Ramírez-Valverde et al, 2014b).…”

Investigación Asociada Con Las Evaluaciones Genéticas De Bovinos Productores De Leche en México

“…INTERBULL (2001) recomienda usar FA para efectos que necesiten ajustes multiplicativos, y los que requieran ajustes aditivos deberán incluirse en el modelo. En las EG de bovinos lecheros en México, se realizan ajustes para producción de leche, grasa y proteína a 305 d, con excepción de la raza SA, la cual se ajusta a 210 d (Ramírez-Valverde et al, 2014b;2016); mientras que algunas publicaciones reportan ajustes por equivalente maduro y número de ordeños (Valencia et al, 2008a;Ramírez-Valverde et al, 2010;Toledo et al 2014a). INTERBULL (2001) sugiere que los FA se actualicen al menos cada generación.…”

“…Magaña-Valencia et al (2013) determinaron que el método más apropiado para determinar conectividad en EG de bovinos es el número de lazos genéticos directos totales debido a sementales y hembras comunes. En los estudios revisados, los pocos que indicaron el análisis de conectividad (Ramírez-Valverde et al, 2010;Ruíz-Flores et al, 2011) emplearon el programa AMC (Roso y Schenkel, 2006), el cual considera el total de lazos genéticos en función del número y tipo de relaciones de parentesco entre animales en diferentes GC.…”

“…En México se han estudiado diversos modelos para mejorar las predicciones de los VG, como son: inclusión de diferentes efectos fijos o aleatorios, uso de modelos uni-o multivariados y uso de modelos del día de prueba o regresión aleatoria. Los programas más utilizados para realizar EG han sido MTDFREML (Valencia et al, 2008a;Ramírez-Valverde et al, 2010;Ríos-Utrera et al, 2012), ASREML (López-Ordaz et al, 2009;Montaldo et al, 2010;Toledo et al, 2014) y AIREML (Santellano-Estrada et al, 2011).…”